ArticleSensors (Basel, Switzerland)2019
Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Neuroprotective effects of ursodeoxycholic acid in Parkinson's disease and Alzheimer's disease.Neuroprotection (Chichester, England) · 2026Review
- Will adaptive deep brain stimulation for Parkinson's disease become a real option soon? A Delphi consensus study.NPJ Parkinson's disease · 2025Article
- Development of machine learning models for mortality risk prediction after cardiac surgery.Cardiovascular diagnosis and therapy · 2022Article
- Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease.PLoS computational biology · 2021Article
- Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review.Bioelectricity · 2020Review
- Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review.Brain sciences · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using ε -recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that ε -recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system.
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Registered trials
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